Image searching technologies may enable a user to obtain information about an object in an image or locate a specific object within the image. The same process may be applied to people, scenes, text, etc. Typical image recognition services operate by receiving an image from the user, analyzing the image for distinctive features, and then matching the object in the image against images in a database using algorithms.
As digital camera sensors and memory capacity have improved, the sizes of the images captured by digital cameras have increased. Currently, some camera-equipped smartphones capture images of over 40 megapixels. Uploading an image of this size to a cloud-based service usually takes significant time and bandwidth, especially if done over a cellular network, which often incurs additional cost to the user. Once such a large image is uploaded, an image recognition service may take extra time and computational power to process the image as compared to a smaller image, which slows response time down. Additionally, since the image is sent over a network, issues related to privacy can arise. As a result, significant challenges exist for cloud-based image search services to be applied to large images captured on next generation cameras.